Adaptive engine with identity mapping modules

Abstract

An adaptive controller and method for adaptive control. The method comprises receiving a plurality of error signals and an input regressor, applying one or more estimation laws to the error signals and the input regressor to produce an estimated model parameter tensor, generating a plurality of subcomponents of a possible control signal, combining two or more subcomponents of the plurality of subcomponents of the possible control signal to produce the possible control signal, generating an estimated system output, and selecting a preferred control signal from a set comprising the possible control signals or a best combination of possible control signals blended from two or more possible control signals of the set.

What is claimed is: 
     
         1 . Adaptive engine configured to receive a reference signal and provide a control to one or more actuators, the adaptive engine comprising:
 an estimation law module, the estimation law module configured to receive a plurality of error signals and an input regressor, the input regressor comprising:
 a reference signal, r, 
 a system output measurement, y meas , 
 a control output, u out_k−1 , from a previous iteration, 
 one or more estimated system outputs, wherein the one or more estimated system outputs comprise one or more of an estimated system output for a current iteration, y est_SE , and an estimated system output for a previous iteration, y est_out , the estimation law module configured to: 
 apply one or more estimation laws to the error signals and the input regressor to produce an estimated model parameter tensor, Θ, for a nonlinear model of the one or more actuators or a power system controlled by the one or more actuators, wherein the estimated model parameter tensor, Θ, comprises a plurality of subcomponents; 
   a control law sub-engine, wherein the control law sub-engine comprises a plurality of mapping modules, wherein each of the plurality of mapping modules is configured to receive at least a portion of the plurality of subcomponents of the estimated model parameter tensor, Θ, and output at least one of:
 (1) a subcomponent of a possible control signal, wherein each subcomponent of the possible control signal is based on a portion of the input regressor and at least one subcomponent of the estimated model parameter tensor, and 
 (2) at least one error signal of the plurality of error signals; 
 wherein the control law sub-engine combines two or more subcomponents of the possible control signal to produce the possible control signal, u SE ; 
 wherein a non-linear model is used to produce a possible estimated system output, y est_SE , based upon applying the nonlinear model to (1) the possible control signal, u SE , or (2) the control output, u out_k−1 , from the previous iteration; and 
   a selector module configured to receive the possible control signal, u SE , and the possible estimated system output, y est_SE .   
     
     
         2 . The adaptive engine of  claim 1 , wherein each of the plurality of subcomponents of the estimated model parameter tensor, Θ, corresponds to an estimated model parameter tensor coefficient. 
     
     
         3 . The adaptive engine of  claim 2 , where the estimation law module is configured to pass a plurality of estimated model parameter tensor coefficients to a first mapping module of the plurality of mapping modules, and wherein the first mapping module is configured to:
 approximate, using one or more of the plurality of estimated model parameter tensor coefficients, a noise and disturbance component, u nd_approx , in one or more of a possible control signal, u SE_k−1 , from a previous iteration and the control output, u out_k−1 , from the previous iteration; and   determine, using a first estimated model parameter tensor coefficient, a measured noise and disturbance component, u nd_k .   
     
     
         4 . The adaptive engine of  claim 3 , wherein the control law sub-engine is configured to:
 determine, using a second estimated model parameter tensor coefficient, noise and disturbance effects, ω y_nd , in the system output measurement, y meas ;   and wherein determining the measured noise and disturbance component, u nd_k , comprises:   mapping, using the first estimated model parameter tensor coefficient, the noise and disturbance effects, ω y_nd , to the measured noise and disturbance component, u nd_k .   
     
     
         5 . The adaptive engine of  claim 3 , wherein the estimation law module is configured to adapt the plurality of estimated parameter tensor coefficients to minimize an error, u nd_error , corresponding to a difference between u nd_k  and u nd_approx . 
     
     
         6 . The adaptive engine of  claim 2 , wherein the estimation law module is configured to:
 pass a first estimated model parameter tensor coefficient, θ ε , and a second estimated model parameter tensor coefficient, θ Ψ , to a stabilization mechanism module of the control law sub-engine;   pass the second estimated model parameter tensor coefficient, θ Ψ , to a stability hypothesis tester of the stabilization mechanism module;   and wherein the stability hypothesis tester is configured to map, using the second estimated model parameter tensor coefficient, θ Ψ , an input control signal component, ω u_in , to a predicted system output.   
     
     
         7 . The adaptive engine of  claim 6 , wherein the stability hypothesis tester is configured to:
 determine if the predicted system output is stable or unstable; and   one of:
 upon determining that the predicted system output is unstable, 
   stabilize the input control signal component, ω u_in ; or
 upon determining that the predicted system output is stable, pass the input control signal component, ω u_in , to a non-linearities compensation module of the adaptive engine. 
   
     
     
         8 . The adaptive engine of  claim 7 , wherein,
 the estimation law module is configured to adapt the first estimated model parameter tensor coefficient, θ ε , and pass the adapted first estimated model parameter tensor coefficient, θ ε , to the stabilization mechanism module,   and wherein the input control signal component, ω u_in , is stabilized using the adapted first estimated model parameter tensor coefficient, θ ε .   
     
     
         9 . The adaptive engine of  claim 8 , wherein, upon determining that the predicted system output is unstable, the stabilization mechanism module passes a stabilized input control signal component, ω u_in_stabilized , to the non-linearities compensation module. 
     
     
         10 . The adaptive engine of  claim 9 , wherein the non-linearities compensation module produces the possible control signal, u SE , using one of the input control signal component, ω u_in , and the stabilized input control signal component, ω u_in_stabilized . 
     
     
         11 . The adaptive engine of  claim 1 , wherein the plurality of subcomponents of the estimated model parameter tensor, Θ, comprise a plurality of estimated model parameter tensor coefficients for mapping control signals to estimated or predicted system output measurements. 
     
     
         12 . The adaptive engine of  claim 1 , wherein at least one subcomponent of the plurality of subcomponents of the estimated model parameter tensor, Θ, comprises an estimated model parameter tensor coefficient for mapping the reference signal, r, to a desired control signal, u r_des_k . 
     
     
         13 . The adaptive engine of  claim 1 , wherein at least one subcomponent of the plurality of subcomponents of the estimated model parameter tensor, Θ, corresponds to an estimated delay increment or an estimated delay decrement, and wherein the estimated delay increment or decrement enables control of unstable zero dynamics. 
     
     
         14 . The adaptive engine of  claim 1 , wherein the adaptation and estimation law module is optimized for unstable zero dynamics situations seen in nonlinear systems. 
     
     
         15 . The adaptive engine of  claim 1 , wherein the adaptation and estimation law module is configured to achieve rapid convergence during stable and unstable zero-dynamics. 
     
     
         16 . The adaptive engine of  claim 1 , wherein,
 the selector module is configured to receive one or more other possible control signals from one or more other control law sub-engines,   the selector module further comprises a tensor synchronization and coherency module configured to receive an exogenous signal, and   the exogenous signal is applied to the possible control signal, u SE_k , and the one or more other possible control signals to smooth transitions between possible control signals input to the selector module.   
     
     
         17 . A method for adaptive control, the method comprising:
 receiving a plurality of error signals and an input regressor, the input regressor comprising:
 a reference signal, r, 
 a system output measurement, y meas , 
 a control output, u out_k−1 , from a previous iteration, 
 one or more estimated system outputs, wherein the one or more estimated system outputs comprise one or more of an estimated system output for a current iteration, y est_SE , and an estimated system output for a previous iteration, y est_out ; 
   applying one or more estimation laws to the error signals and the input regressor to produce an estimated model parameter tensor, Θ, for a nonlinear model of one or more actuators or a power system controlled by the one or more actuators, wherein the estimated model parameter tensor, Θ, comprises a plurality of subcomponents;   generating a plurality of subcomponents of a possible control signal, wherein each subcomponent of the possible control signal is based on a portion of the input regressor and at least one subcomponent of the estimated parameter tensor;   combining two or more subcomponents of the plurality of subcomponents of the possible control signal to produce the possible control signal, u SE_k ;   generating an estimated system output, y est_SE , based upon applying the nonlinear model to (1) the possible control signal, u SE_k , or (2) the control output, u out_k−1 , from the previous iteration; and   selecting (1) a best possible control signal, u SE , from a set comprising the possible control signal, u SE_k , and a plurality of other possible control signals, or (2) a best combination of possible control signals, u SE , blended from two or more possible control signals of the set,   wherein each of the plurality of other possible control signals is associated with an estimated system output, y est_SE , and wherein the selecting is based at least in part on a predicted error derived from each of the estimated system outputs, y est_SE .   
     
     
         18 . A non-transient computer-readable storage medium having instructions embodied thereon, the instructions being executable by one or more processors to perform a method for adaptive control, the method comprising:
 receiving a plurality of error signals and an input regressor, the input regressor comprising:
 a reference signal, r, 
 a system output measurement, y meas , 
 a control output, u out_k−1 , from a previous iteration, 
 one or more estimated system outputs, wherein the one or more estimated system outputs comprise one or more of an estimated system output for a current iteration, y est_SE , and an estimated system output for a previous iteration, y est_out ; 
   applying one or more estimation laws to the error signals and the input regressor to produce an estimated model parameter tensor, Θ, for a nonlinear model of one or more actuators or a power system controlled by the one or more actuators, wherein the estimated model parameter tensor, Θ, comprises a plurality of subcomponents;   generating a plurality of subcomponents of a possible control signal, wherein each subcomponent of the possible control signal is based on a portion of the input regressor and at least one subcomponent of the estimated model parameter tensor;   combining two or more subcomponents of the plurality of subcomponents of the possible control signal to produce the possible control signal, u SE_k ;   generating an estimated system output, y est_SE , based upon applying the nonlinear model to (1) the possible control signal, u SE_k , or (2) the control output, u out_k−1 , from the previous iteration; and   selecting (1) a best possible control signal, u SE , from a set comprising the possible control signal, u SE_k , and a plurality of other possible control signals, or (2) a best combination of possible control signals, u SE , blended from two or more possible control signals of the set,   wherein each of the plurality of other possible control signals is associated with an estimated system output, y est_SE , and wherein the selecting is based at least in part on a predicted error derived from each of the estimated system outputs, y est_SE .